The detection of correspondences between regions in different images is important in many image processing applications. The search for such corresponding regions can be simplified by use of invariant or covariant region properties, that is, detected properties that transform in a known way under viewpoint changes. An invariant measure of salience can be used for example to ensure that substantially the same, most salient regions from different images will be used. Such an invariant measure of salience is described in an article by E. Ranguelova et al, titled “Morphology-based Stable Salient Regions Detector” and published at the International conference on Image and Vision Computing New Zealand (IVCNZ '06), 2006, pp. 97-102 (EPO reference XP-002540172). The article proposes to compute a measure of “salience” that has been found to approximate human perception of salient image features. In addition to salience, each region is characterized by its moments, which can be considered to define a central position and an ellipse around that position that forms a first order approximation for the shape of the region. Under the assumption that the effect of viewpoint changes can be approximated by affine transformations, the effect of viewpoint changes on the moments is well defined.
The resulting region descriptors of regions in a first and second image can be used to search for corresponding regions from the first and second image. Regions having sufficient salience in both images and moments that match but for an affine transformation can be identified as corresponding regions. However, this type of matching does not account for the context of the identified regions. When attempts are made to use the context, the invariance property is usually lost, so that context sensitive matching is not robust against viewpoint changes.
A method of image feature matching is disclosed in an article by HongLi Deng et al, titled “Reinforcement Matching Using Region Context”, published in at the conference on Computer Vision and Pattern Recognition 2006, New York, June 2006 pages 1-8 (EPO reference XP010922749). HongLi Deng et al disclose a method to compute match scores between detected regions in pairs of images. Regions are detected and for the detected regions ellipses are defined oriented along the dominant gradient orientation of the regions. The spatial context of the region is normalized using a moments matrix. Next surrounding matching regions in the context of the region are counted by binning, using bins defined in terms of the normalized context. A count is made of matching surrounding regions from contexts of a pair of matched regions. This count of surrounding regions is used to reinforce the match scores for the regions that define the context themselves. Thus, Hongli Deng et al look for matches with all regions anywhere in the context of a region. When a region in a context in one image cannot be matched to a region in a corresponding context in the other image, this does not reinforce the match, but otherwise it has no effect.